mlsanityData to 14 Oct 2026

A Practical Recipe for Federated Learning Under Statistical Heterogeneity Experimental Design

arXiv 2307.15245 ↗First posted 28 Jul 2023cs.LG cs.AIcited by 5

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Abstract (arXiv)

Federated Learning (FL) has been an area of active research in recent years. There have been numerous studies in FL to make it more successful in the presence of data heterogeneity. However, despite the existence of many publications, the state of progress in the field is unknown. Many of the works use inconsistent experimental settings and there are no comprehensive studies on the effect of FL-specific experimental variables on the results and practical insights for a more comparable and consistent FL experimental setup. Furthermore, the existence of several benchmarks and confounding variables has further complicated the issue of inconsistency and ambiguity. In this work, we present the first comprehensive study on the effect of FL-specific experimental variables in relation to each other and performance results, bringing several insights and recommendations for designing a meaningful and well-incentivized FL experimental setup. We further aid the community by releasing FedZoo-Bench, an open-source library based on PyTorch with pre-implementation of 22 state-of-the-art methods, and a broad set of standardized and customizable features available at https://github.com/MMorafah/FedZoo-Bench. We also provide a comprehensive comparison of several state-of-the-art (SOTA) methods to better understand the current state of the field and existing limitations.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 62 of its 88 references that are in the map. First posted on arXiv 28 Jul 2023.

Builds on

62 of 88 references.

References · 62

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38 more
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Cited by year

Citing papers per year: 2024: 3; 2025: 2.

5 papers.

  1. 2024Non-IID data in Federated Learning: A Survey with Taxonomy, Metrics, Methods, Frameworks and Future Directionscited by 13
  2. 2024Towards Diverse Device Heterogeneous Federated Learning via Task Arithmetic Knowledge Integrationcited by 5
  3. 2024Federated Learning Client Pruning for Noisy Labelscited by 4
  4. 2025LLM-QFL: Distilling Large Language Model for Quantum Federated Learningcited by 0
  5. 2025Avoid Forgetting by Preserving Global Knowledge Gradients in Federated Learning with Non-IID Datacited by 0